Andrea Kaplan
March 27, 2014
A web-based tool for community detection in networks
Who cares and why did we make this?
\[ Q = \sum\limits_r (e_{rr} - a_r^2) \]
\( r = \) a community
\( e_{rr} = \) fraction of links that connect two nodes inside the community
\( a_r = \) the fraction of links that have one or both vertices inside the community
\( Q = 0 \): number of within-community edges no better than random
\( Q \in [0.3, 0.7] \): strong community structure
\( Q=1 \): maximum value
\[ \pi(S) = \frac{\#\text{ of edges within } S}{\#\text{ of edges leaving} S} \]
\[ \phi(S) = \frac{\#\text{ of edges leaving} S}{\sum\limits_{u \in S} \text{degree of } u} \]
The main elements of the problem themselves [graph clustering], i.e. the concepts of community and partition, are not rigorously defined, and require some degree of arbitrariness and/or common sense. (Fortunato, 2010)
Meet gravicom
(1) Control Panel, (2) Data Management, (3) Connection Table, (4) Graph Display, and (5) Tabset
http://shiny1.stat.iastate.edu/ajkaplan/gravicom/ (must be VPNed to internal ISU network)
Theory behind the curtain
What makes it tick?
GML file structure:
graph
[
directed 0
node
[
id 0
label "Node 1"
value 100
]
node
[
id 1
label "Node 2"
value 200
]
edge
[
source 1
target 0
]
]
JSON file structure:
{
"nodes":
[{"id":"n0","v_id":"0","v_label":"Node 1","v_value":"100"},
{"id":"n1","v_id":"1","v_label":"Node 2","v_value":"200"}],
"edges":
[{"source":0, "target":1}]
}
Possible extensions to gravicom
Thank you!